Robot and Servo Drive Lab.
Regenerative Braking System of Electric Vehicle
Driven by Brushless DC Motor
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 61, NO. 10,
OCTOBER 2014 By Xiaohong Nian, Fei Peng, and Hang Zhang
Department of Electrical Engineering
Southern Taiwan University of Science and Technology
2016/3/24
Outline
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 Simulation Results
Abstract
 Conclusion
Introduction
 References
Motor And Control
BLDC Motors
BLDC Motor Control
MOSFET Control of Regenerative
EV Modeling
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Optimal Braking Performacne And RBS Efficiency
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Abstract

Regenerative braking can improve energy usage efficiency and can
prolong the driving distance of electric vehicles (EVs). In this paper,
BLDC motor control utilizes the traditional roportional–integral–derivative
(PID) control.

The simulation results show that the fuzzy logic and PID control can realize
the regenerative braking and can prolong the driving distance of EVs under
the condition of ensuring braking quality. At last, it is verified that the
proposed method is realizable for practical implementation.
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Introduction
Regenerative braking can be used in EVs as a process for
recycling the brake energy, which is impossible in the
conventional internal combustion vehicles.
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BLDC Motors

Brushless dc (BLDC) motors are ideally suitable for Evs
because of their high power densities, good speed-torque
characteristics, high efficiency, wide speed ranges, and low
maintenance.

BLDC motor is a type of synchronous motor. It means that the
magnetic field generated by the stator and the magnetic field
generated by the rotor rotation are at the same frequency.
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BLDC Motors

Knowledge of rotor position is critical to sustaining the motion
of the windings correctly. The information of rotor motion is
obtained either from Hall effect sensors or from coil EMF
measurements
Fig. 2.
Fig. 2. Back EMF BLDC
motor phase.
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BLDC Motor Control

BLDC motor control is the main control of the electronic
commutator (inverter), and the commutation is achieved by
controlling the order of conduction on the inverter bridge arm
a typical H-bridge is shown in Fig. 3.
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Fig. 3. H-bridge inverter circuit.
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BLDC Motor Control
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The BLDC voltage vector is divided into six sectors, which is
just a one-to-one correspondence with the Hall signal six tates,
as illustrated.
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MOSFET Control of Regenerative

Regenerative braking can be achieved by the reversal of
current in the motor-battery circuit during deceleration, taking
advantage of the motor acting as a generator, redirecting the
current flow into the supply battery.

Due to the presence of inductances in motor windings, these
inductances in the motor can constitute the boost circuit.
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MOSFET Control of Regenerative

Fig. 5 shows the phase relation among the back EMF, the
armature current of the BLDC motor, and the switching signals
for the bidirectional dc/ac converter.
Fig. 5. Regenerative braking
with single switch.
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
By controlling MOSFET, the whole circuit constitutes a boost
circuit.
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Fig. 6. Equivalent circuit of the single switch.
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
According to the principle of the volt-second balance, one can
conclude that the net change in the equivalent inductor voltage
vL is zero over one electric cycle, i.e.,
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Fig. 7. Maximum conversion ratio versus K for regenerative braking with single switch.
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EV Modeling

The modeling of the EV has been done in MATLAB/ Simulink.
The driver block makes a torque request which propagates
through various powertrain system component and realizes
vehicle motion.
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Fig.
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8. Structure of the control strategy system.
Where mv is the vehicle mass (in kilograms), v is the vehicle
speed (in meters per square second), Fa is the aerodynamic
friction (in newtons), Fr is the rolling friction (in newtons), and
Fg is the force caused by gravity when driving on nonhorizontal
roads (in newtons).
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Aerodynamic Friction Losses:
Rolling Friction Losses:
Uphill Driving Force:
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Fuzzy Control

The fuzzy control strategy of the EV braking force distribution
structure is shown in Fig. 8; the three inputs are the EV frontwheel braking force, speed, and battery charge state [state of
charge (SOC)].
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Membership functions of fuzzy control. (a) Membership function of
the front braking force. (b) Membership function of the SOC. (c) Membership
function of speed. (d) Membership function of ratio.
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Fuzzy control rules: the front braking force is L, M, and H;
SOC is L, M, and H; and speed is L and H. We prefer the rules
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PID Control

With PID control used primarily to ensure a constant brake
torque, different braking force values will give different
PWMs. It is supposed that PID control can quickly adjust the
desired PWM in order to maintain braking torque constantly.

When the fuzzy reasoning is slower than PID control, the
braking torque can be real-time controlled by PID control .
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Optimal Braking Performacne And RBS Efficiency

The fully controllable hybrid brake system can be controlled to
apply braking forces on the front and rear wheels by following
the ideal braking force distribution curve (Fig. 11).
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
The following equations describe the battery’s SOC at discharge
and charge. At discharge
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
In the braking process on a flat road, the vehicle’s kinetic
energy and regenerative electrical energy are calculated by the
following:
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Simulation Results
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Simulation EV speed curve.
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Simulation EV speed curve.
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Simulation EV speed curve.
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Braking force distribution.
Energy regeneration when braking.
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Current curve on the BLDC motor dc bus with PID control.
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Battery’s SOC change.
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DC bus voltage of different duties but at the same speed.
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DC bus voltage at the same speed.
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Voltage, current, and speed waveforms at the breaking state.
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Conclusion

This paper has presented the RBS of EVs which are driven by
the BLDC motor. The performance of the EVs’ egenerative
brake system has been realized by our control scheme which
has been implemented both in the simulation and in the
experiments.

Therefore, it can be concluded that this RBS has the ability to
recover energy and ensure the safety of braking in different
situations.
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References
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Thanks for listening
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